Real-world Human Re-identification: Attributes and Beyond.

PhDSurveillance systems capable of performing a diverse range of tasks that support human intelligence and analytical efforts are becoming widespread and crucial due to increasing threats upon national infrastructure and evolving business and governmental analytical requirements. Surveillance data can be critical for crime-prevention, forensic analysis, and counter-terrorism activities in both civilian and governmental agencies alike. However, visual surveillance data must currently be parsed by trained human operators and therefore any utility is offset by the inherent training and staffing costs as a result. The automated analysis of surveillance video is therefore of great scientific interest. One of the open problems within this area is that of reliably matching humans between disjoint surveillance camera views, termed re-identification. Automated re-identification facilitates human operational efficiency in the grouping of disparate and fragmented people observations through space and time into individual personal identities, a pre-requisite for higher-level surveillance tasks. However, due to the complex nature of realworld scenes and the highly variable nature of human appearance, reliably re-identifying people is non-trivial. Most re-identification approaches developed so far rely on low-level visual feature matching approaches that aim to match human detections against a known gallery of potential matches. However, for many applications an initial detection of a human may be unavailable or a low-level feature representation may not be sufficiently invariant to photometric or geometric variability inherent between camera views. This thesis begins by proposing a “mid-level” human-semantic representation that exploits expert human knowledge of surveillance task execution to the task of re-identifying people in order to compute an attribute-based description of a human. It further shows how this attribute-based description is synergistic with low-level data-derived features to enhance re-identification accuracy and subsequently gain further performance benefits by employing a discriminatively learned distance metric. Finally, a novel “zero-shot” scenario is proposed in which a visual probe is unavailable but re-identification is still possible via a manually provided semantic attribute description. The approach is extensively evaluated using several public benchmark datasets. One challenge in constructing an attribute-based and human-semantic representation is the requirement for extensive annotation. Mitigating this annotation cost in order to present a realistic and scalable re-identification system, is motivation for the second technical area of this thesis, where transfer-learning and data-mining are investigatedin two different approaches. Discriminative methods trade annotation cost for enhanced performance. Because discriminative person re-identification models operate between two camera views, annotation cost therefore scales quadratically on the number of cameras in the entire network. For practical re-identification, this 4 is an unreasonable expectation and prohibitively expensive. By leveraging flexible multi-source transfer of re-identification models, part of this cost may be alleviated. Specifically, it is possible to leverage prior re-identification models learned for a set of source-view pairs (domains), and flexibly combine those to obtain good re-identification performance for a given target-view pair with greatly reduced annotation requirements. The volume of exhaustive annotation effort required for attribute-driven re-identification scales linearly on the number of cameras and attributes. Real-world operation of an attributeenabled, distributed camera network would also require prohibitive quantities of annotation effort by human experts. This effort is completely avoided by taking a data-driven approach to attribute computation, by learning an effective associated representation by crawling large volumes of Internet data. By training on a larger and more diverse array of examples, this representation is more view-invariant and generalisable than attributes trained on conventional scales. These automatically discovered attributes are shown to provide a valuable representation that significantly improves re-identification performance. Moreover, a method to map them onto existing expert-annotated-ontologies is contributed. In the final contribution of this thesis, the underlying assumptions about visual surveillance equipment and re-identification are challenged and the thesis motivates a novel research area using dynamic, mobile platforms. Such platforms violate the common assumption shared by most previous research, namely that surveillance devices are always stationary, relative to the observed scene. The most important new challenge discovered in this exciting area is that the unconstrained video is too challenging for traditional approaches to applying discriminative methods that rely on the explicit modelling of appearance translations when modelling view-pairs, or even a single view. A new dataset was collected by a remote-operated vehicle using control software developed to simulate a fully-autonomous re-identification unmanned aerial vehicle programmed to fly in proximity with humans until images of sufficient quality for re-identification are obtained. Variations of the standard re-identification model are investigated in an enhanced re-identification paradigm, and new challenges with this distinct form of re-identification are elucidated. Finally, conventional wisdom regarding re-identification in light of these observations is re-examined

Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

Models, Simulations, and the Reduction of Complexity

Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

Models, Simulations, and the Reduction of Complexity

Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

Music Composed For Calm And Catharsis Using A Compositional Toolkit For Emotional Evocation - Inspired By And Directed Towards Healthcare Contexts And Self-Managed Wellness

Emotional experience through music listening is a universal experience. In the age of COVID-19 and an ever-mentally enslaved population, music that encourages calm and/or catharsis is more relevant than ever (Gallagher et al., 2020). As composers, can we form a framework for and create music to pointedly evoke an intentional emotion? This dissertation seeks to build on the solid foundation of music and emotion researchers’ past theories, and demonstrate how to further utilise the power that music has in both our everyday lives, and also in healthcare settings – providing an output of a large suite of music for use for calm and catharsis, and a Compositional Toolbox for Emotional Evocation that composers might use to effect positive emotional change. In two pilot studies: one for children and one for adults, this dissertation tests music written using said Toolbox, to observe its effect on arousal and pleasure. The studies also utilise visuals as a secondary means of sensory control, and to investigate whether the multisensory application of music and visuals enhances emotional evocation over isolated experience. Participants rated on a Likert-type scale, how they think each sample would make someone feel, or how it made them feel. An analysis of pieces from these studies is included in this dissertation. Mixed-method, deductive, and thematic analysis was used for data, which was collected via surveys and interviews. It was found that music using the Toolbox was more emotionally evocative, more calming, and happier overall than that written without. Most of the pieces achieved their emotional aims, and positive correlations between the use of music and visuals together have arisen. Music without the visuals appeared to be calmer than that with visuals in one of the studies. This dissertation begins to promote the use of the Compositional Toolbox for Emotional Evocation as a framework for emotional composition

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario

On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models

284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity